import pandas as pd
import numpy as np
import yfinance as yf
import random
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
import secrets
import plotly as plotly
import plotly.express as px
from tqdm import tqdm
import plotly.graph_objects as go
pd.set_option('plotting.backend', 'matplotlib')
plotly.offline.init_notebook_mode()
#warnings.simplefilter(action='ignore', category=FutureWarning)

TICKER="IWDA.AS"
NUMBER_OF_SIMULATIONS=1000
STARTING_CAPITAL = 10000
YEARS_OF_SIMULATION=30

YEARS_OF_SIMULATION=YEARS_OF_SIMULATION+1
Market_Days=253

df=yf.download(TICKER)["Adj Close"].pct_change(1).dropna()

[*********************100%%**********************]  1 of 1 completed

df.plot()

<Axes: xlabel='Date'>

df

Date
2009-09-28    0.000000
2009-09-29    0.000000
2009-09-30    0.000000
2009-10-01    0.000000
2009-10-02    0.000000
                ...   
2023-11-13    0.007003
2023-11-14    0.006568
2023-11-15    0.003582
2023-11-16   -0.005737
2023-11-17    0.003590
Name: Adj Close, Length: 3624, dtype: float64

df_simulations=np.zeros((YEARS_OF_SIMULATION,NUMBER_OF_SIMULATIONS))
df_simulations[0,:]=STARTING_CAPITAL

for x in tqdm(range(0,NUMBER_OF_SIMULATIONS)):
    for i in range(1,YEARS_OF_SIMULATION):
        annual_change=1
        for k in range(Market_Days):
            annual_change=annual_change*(1+(secrets.choice(df.iloc[:])))
        df_simulations[i,x]=annual_change*df_simulations[i-1,x]
df_simulations= pd.DataFrame(df_simulations)
df_simulations_1=  pd.DataFrame(df_simulations)

  0%|          | 0/1000 [00:00<?, ?it/s]C:\Users\dadoi\anaconda3\Lib\random.py:374: FutureWarning:

Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`

100%|██████████| 1000/1000 [03:26<00:00,  4.85it/s]

df_simulations_1

df_simulations=df_simulations_1
df_simulations.iloc[YEARS_OF_SIMULATION-1]

0      8.352580e+04
1      1.178388e+05
2      3.831392e+05
3      1.765340e+05
4      1.819640e+05
           ...     
995    2.283283e+05
996    2.291882e+05
997    1.036773e+05
998    1.046329e+06
999    4.776593e+04
Name: 30, Length: 1000, dtype: float64

quantile=df_simulations.iloc[YEARS_OF_SIMULATION-1].quantile([0.1,0.9])
quantile=list(quantile)
print(quantile)
df_simulations_purged=df_simulations[df_simulations.columns[ df_simulations.max() < quantile[1]]]
df_simulations_purged=df_simulations_purged[df_simulations_purged.columns[ df_simulations_purged.max() > quantile[0]]]

[77447.84532902476, 743787.8775983766]

df_simulations_purged.plot(figsize=(16,8), title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios", legend=False)

<Axes: title={'center': 'Simulation of 1000 portfolios'}>

df_simulations_purged.iloc[YEARS_OF_SIMULATION-1].plot.density(figsize=(16,8),fontsize=14, xlim=(-100000,900000))

<Axes: ylabel='Density'>

top_25 =[]
low_25 =[]
median=[]
for i in range(0,YEARS_OF_SIMULATION):
    top_25.append(df_simulations.iloc[i].quantile(0.75))
    low_25.append(df_simulations.iloc[i].quantile(0.25))
    median.append(df_simulations.iloc[i].median())
columns=["top 25%","median","bottom 25%"]
df_statistics=pd.DataFrame(list(zip(top_25,median,low_25)),columns=columns)
df_statistics

#ax= df_statistics.plot(legend=None,logy=False,fontsize=20,figsize=(25,15),linewidth=4,color="black",title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios")
pd.set_option('plotting.backend', 'plotly')

fig = df_statistics.plot(width=1600, height=800,title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios",labels= {"index": "Years to Maturity", "value":"Total Capital"} )
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(top_25),fill='tonexty',mode='lines', line_color='blue' , fillcolor ="red", showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(median),fill='tonexty',mode='lines', line_color='orange', fillcolor="green", showlegend=False, hoverinfo="skip"))

ls_simulations_at_profit=[]
for i in range(YEARS_OF_SIMULATION):
    c=len([1 for i in list(df_simulations.iloc[i])if i > STARTING_CAPITAL])
    ls_simulations_at_profit.append(c/NUMBER_OF_SIMULATIONS*100)
pd.set_option('plotting.backend', 'plotly')
df_simulations_at_profit = pd.DataFrame(ls_simulations_at_profit,columns=["Profit %"])
df_simulations_at_profit.plot(title="Minimum investment horizon", labels= {"index": "Period length in years", "value":"Chance to be in profit"})

TICKER="^GSPC"

df=yf.download(TICKER)["Adj Close"].pct_change(1).dropna()
df_simulations = np.zeros((YEARS_OF_SIMULATION, NUMBER_OF_SIMULATIONS))
df_simulations[0, :] = STARTING_CAPITAL

for x in tqdm(range(0, NUMBER_OF_SIMULATIONS)):
    for i in range(1, YEARS_OF_SIMULATION):
        annual_change = 1
        for k in range(Market_Days):
            annual_change = annual_change * (1 + (secrets.choice(df.iloc[:])))
        df_simulations[i, x] = annual_change * df_simulations[i - 1, x]
df_simulations = pd.DataFrame(df_simulations)
df_simulations_1 = pd.DataFrame(df_simulations)
df_simulations = df_simulations_1
df_simulations.iloc[YEARS_OF_SIMULATION - 1]
quantile = df_simulations.iloc[YEARS_OF_SIMULATION - 1].quantile([0.1, 0.9])
quantile = list(quantile)
df_simulations_purged = df_simulations[df_simulations.columns[df_simulations.max() < quantile[1]]]
df_simulations_purged = df_simulations_purged[df_simulations_purged.columns[df_simulations_purged.max() > quantile[0]]]
top_25 =[]
low_25 =[]
median=[]
for i in range(0,YEARS_OF_SIMULATION):
    top_25.append(df_simulations.iloc[i].quantile(0.75))
    low_25.append(df_simulations.iloc[i].quantile(0.25))
    median.append(df_simulations.iloc[i].median())
columns=["top 25%","median","bottom 25%"]
df_statistics=pd.DataFrame(list(zip(top_25,median,low_25)),columns=columns)
pd.set_option('plotting.backend', 'plotly')

fig = df_statistics.plot(width=1600, height=800,title=f"Simulation of {NUMBER_OF_SIMULATIONS} portfolios",labels= {"index": "Years to Maturity", "value":"Total Capital"} )
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(top_25),fill='tonexty',mode='lines', line_color='blue' , fillcolor ="red", showlegend=False, hoverinfo="skip"))
fig.add_trace(go.Scatter(x=list(range(0,YEARS_OF_SIMULATION)),y=np.array(median),fill='tonexty',mode='lines', line_color='orange', fillcolor="green", showlegend=False, hoverinfo="skip"))

[*********************100%%**********************]  1 of 1 completed

  0%|          | 0/1000 [00:00<?, ?it/s]C:\Users\dadoi\anaconda3\Lib\random.py:374: FutureWarning:

Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`

100%|██████████| 1000/1000 [03:28<00:00,  4.80it/s]

ls_simulations_at_profit = []
for i in range(YEARS_OF_SIMULATION):
    c = len([1 for i in list(df_simulations.iloc[i]) if i > STARTING_CAPITAL])
    ls_simulations_at_profit.append(c / NUMBER_OF_SIMULATIONS * 100)

pd.set_option('plotting.backend', 'plotly')
df_simulations_at_profit = pd.DataFrame(ls_simulations_at_profit, columns=["Profit %"])
df_simulations_at_profit.plot(title="Minimum investment horizon",
                              labels={"index": "Period length in years", "value": "Chance to be in profit"})

!jupyter nbconvert --execute --to html Portfolio1.ipynb --HTMLExporter.theme=dark

[NbConvertApp] Converting notebook Portfolio1.ipynb to html
0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
[NbConvertApp] WARNING | Alternative text is missing on 3 image(s).
[NbConvertApp] Writing 4936503 bytes to Portfolio1.html

	0	1	2	3	4	5	6	7	8	9	...	990	991	992	993	994	995	996	997	998	999
0	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	...	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	10000.000000	1.000000e+04	10000.000000
1	10070.354392	8682.127497	13523.052287	7591.196312	12468.430364	13015.263691	11557.535587	12276.561658	11713.033302	8461.744842	...	9333.955410	11076.665088	10873.466957	10453.252583	12291.354000	10056.151617	11008.934831	11017.467306	1.116024e+04	11146.980364
2	9412.933449	10072.868267	12482.186037	7249.872447	13257.488266	15079.337808	14363.861619	11577.543553	12895.309686	8031.585095	...	9304.173739	13079.512415	9439.335214	12702.705704	15091.412060	10584.225239	11396.841040	13117.129564	1.401999e+04	11029.214270
3	10109.339562	9944.894193	13515.929291	9175.127474	14596.936624	19402.906121	16470.849055	11997.620639	14944.332431	10657.215890	...	9382.075262	16976.749969	12516.169298	17963.592871	19746.902322	14428.349244	11471.973089	14848.461212	1.795668e+04	12257.667369
4	11717.157020	8969.493466	16364.226117	12148.035050	12759.710626	25248.863594	15750.828825	17562.336437	12126.707456	13796.836815	...	9685.116994	20439.959811	17971.622237	21279.268671	28545.467602	21242.847240	14323.394123	16593.214463	1.512262e+04	12787.585755
5	14130.235314	11855.257437	17914.600059	12309.897777	18794.025692	33947.883363	18596.283990	16602.135026	14773.965227	18895.113761	...	9607.392149	25308.886399	19139.042018	21490.787568	26458.637009	27136.957205	16407.050803	16837.458128	1.791887e+04	10380.377780
6	12075.108179	13458.192506	27090.035268	12513.526706	14171.593382	32320.265359	22686.440620	17955.397907	14330.228205	28651.322464	...	10361.785394	36318.453085	23311.404703	27040.991051	21232.145408	30395.856185	19883.787719	21322.358005	1.982593e+04	10978.647701
7	16641.342845	15620.674776	19879.915337	15263.256396	20511.242011	35278.743409	24750.671258	20226.349890	15834.868207	27033.976730	...	9073.055322	40180.083266	23324.241084	32022.268300	18882.879537	28098.738237	19430.832831	27733.639048	2.076939e+04	16314.406606
8	14928.233503	16545.117956	26164.272934	17942.106516	19715.744128	33603.242552	30325.666847	20352.174371	13475.968643	32846.722029	...	9079.173417	43014.278059	28622.037024	36567.444112	21183.865282	27546.286495	22551.346133	33369.601335	2.366700e+04	16148.756594
9	16554.260428	16568.671330	38780.186618	18711.512285	23523.462997	32228.852905	36249.229732	22115.712468	16418.292591	46595.596687	...	10077.107160	57779.358498	36013.015277	34361.592594	21924.211345	28173.462666	24379.808379	32064.014193	3.151071e+04	21678.661551
10	15510.016601	14569.968393	37854.775727	19542.987328	27807.732551	39623.154088	39454.699226	21031.772192	27378.739056	42438.316581	...	10586.195064	79272.314723	48618.072434	37811.166224	19741.825586	28371.714545	31322.039449	31817.865698	4.389916e+04	24206.454391
11	19828.781013	11827.054494	31329.985123	22984.297110	30458.213490	45838.718155	39875.413713	19586.704707	28783.591309	42402.762360	...	10029.016208	112646.216946	57769.920304	45128.460892	24478.349568	30477.989747	33076.156913	32294.587466	5.312072e+04	18466.980090
12	23570.291988	13672.165741	45159.559146	27979.093865	37162.121671	58605.056470	38746.961932	19756.673834	23906.147970	48934.404160	...	12081.500642	123547.231999	81883.581707	40600.790672	26656.916116	27072.624518	38283.869367	27083.815015	7.036555e+04	21049.343862
13	22880.244218	12920.708323	42910.416571	23423.736096	42213.309647	80302.398317	42623.574557	25226.131821	27365.831394	54569.513619	...	19146.976636	139819.089623	94942.314473	39726.687218	42505.890730	36852.022741	47528.879869	31333.596278	1.114560e+05	24756.594270
14	24491.170258	13351.821752	42869.225776	30463.407479	46285.173844	103018.009560	44163.977216	32332.116506	30075.589337	60693.489431	...	16144.821576	151292.973359	112844.528261	36118.523980	46863.104998	37239.092890	50986.367937	33476.041781	1.035880e+05	27235.067222
15	23717.299039	13885.068083	38274.595145	36031.315107	59920.182182	105124.308847	50533.090672	35611.302497	29713.841186	74049.488679	...	22210.662245	184831.560552	100936.523044	38045.406839	46154.247465	36340.891736	61533.788907	31870.337784	1.207622e+05	26509.710454
16	22903.556880	20656.846666	62293.331427	32992.783702	81219.518645	106482.843214	45354.888740	36319.416015	35535.285690	58204.752597	...	27377.274661	168873.464964	114243.686248	33940.693865	47041.664340	37054.892075	70248.198358	45983.544527	1.312317e+05	22313.960909
17	21521.568161	25653.790065	62396.968205	42136.419894	92474.689174	107647.319846	48523.784323	34115.358994	35670.911524	65290.882499	...	27775.686814	163959.381278	125633.635450	40484.497539	59056.320470	45604.603910	91124.209459	48842.426542	1.625908e+05	20434.889156
18	26984.409021	33388.374644	66549.802772	39128.876324	96588.971077	118639.190315	57037.268844	33076.920374	53855.452018	84761.397197	...	34659.002995	202204.251165	123587.657377	42221.672466	59253.400601	41743.545137	86085.515875	49598.556920	2.441315e+05	26872.212466
19	32572.289995	31851.548795	68548.090517	43024.704740	109456.767500	145731.916173	72284.713902	32229.915424	50010.410565	77405.854045	...	35845.844076	231907.175847	125059.566739	35765.647342	63624.712637	40156.367185	119065.403279	48519.461266	1.677412e+05	35929.521911
20	39121.004987	40258.727531	68384.025368	36640.119516	116676.929902	172204.746138	68987.191438	32206.056794	62261.526125	78168.875937	...	31175.338441	207540.577105	205356.605946	40987.421313	84497.103290	38604.219957	131916.439864	42296.800104	1.874008e+05	32298.275500
21	45110.185244	47974.643832	70658.886199	36911.789298	104735.084121	163750.066648	70054.341827	38453.358120	63134.044112	83375.926012	...	34506.382140	209923.620162	219164.553972	44926.949117	86968.939162	48493.306398	157506.366832	50539.921657	2.239283e+05	30274.618709
22	56654.696280	72235.139886	105053.722647	53919.236126	103936.973691	191638.185828	72315.092254	36519.640957	76934.239157	92012.095177	...	51095.558040	195467.731949	205590.081163	67664.845094	96963.364595	44491.968222	171271.656722	49041.809921	3.286572e+05	36439.387737
23	63389.138049	65591.938942	110372.152219	60116.117828	113178.435009	291073.562288	86192.287882	37110.190855	62510.126903	102660.548087	...	46673.303922	254111.326806	260356.956684	106432.066729	105026.694509	56920.134950	174607.516521	53160.565610	3.450173e+05	31605.959343
24	55074.350085	75218.763780	155339.277989	77607.496583	93664.575432	335017.460676	96568.815057	43566.345116	59671.614343	112135.944263	...	49977.703508	347591.720639	291453.260751	120889.088655	129427.262908	65865.455653	151609.363979	65566.940697	3.390865e+05	38582.319652
25	44379.425907	77470.638208	138297.289131	100455.655414	101109.665749	352149.248122	101504.626810	49819.354990	53124.469910	139980.595823	...	49921.471071	471570.416391	275239.860118	155993.859121	110547.789987	90866.373460	162874.820468	83281.993182	2.876401e+05	29606.593096
26	48404.946267	75196.676412	205766.612646	125948.345928	121452.294188	440712.110344	149293.336706	73063.167194	51413.929996	147154.065714	...	54192.093711	492681.447548	338186.205158	217427.269024	113027.635657	107511.968350	168921.254563	74926.301424	3.300912e+05	31885.678573
27	60209.818206	92740.798336	244874.178859	116068.106202	139780.602256	579726.729709	161826.954168	78765.162799	45058.344285	141120.614129	...	61722.892614	488301.578369	395140.193375	256490.058821	131266.996446	126369.891540	158897.022424	106318.001638	4.913907e+05	37581.916255
28	85640.133059	100558.472188	276415.639881	178571.439721	167289.865431	542733.137023	170403.605438	84471.246306	40889.491610	172796.677980	...	65846.841951	455100.282852	438719.131847	308574.881800	173254.300746	165839.880376	173105.532059	89561.252482	6.316132e+05	38156.971694
29	87303.459701	103291.635045	247429.087592	221626.889370	156269.112085	534945.956694	211877.239170	92039.620290	37674.179378	187395.730941	...	65047.355392	567716.722974	466756.223277	399002.864547	184699.889027	202084.081517	208547.880858	75012.539989	8.596206e+05	43589.121470
30	83525.802929	117838.824118	383139.173647	176534.042073	181964.046304	677754.676304	242301.816691	91212.174739	48991.574417	200332.305337	...	70582.669241	542625.368655	462161.895679	474753.858827	176720.519634	228328.330113	229188.180885	103677.308001	1.046329e+06	47765.934421

	top 25%	median	bottom 25%
0	10000.000000	10000.000000	10000.000000
1	12300.068432	11192.206666	9996.354059
2	14336.514808	12458.575130	10590.434376
3	16610.288396	13673.808139	11448.636500
4	18880.224755	15235.844686	12275.881294
5	21268.548352	16847.744814	13104.444773
6	24227.432634	18938.948964	14295.689847
7	28309.627956	20525.367290	15731.353013
8	32458.360955	23276.307390	16845.537477
9	36028.156546	25629.098809	18732.028490
10	40742.400011	28326.933079	20271.160010
11	45823.297530	31436.691970	22479.034417
12	52140.702051	35368.912631	24832.401173
13	58912.698081	39071.737003	27360.383405
14	66291.311819	44200.762285	30142.757603
15	73921.752918	48891.111519	32617.537553
16	85771.487814	55322.310246	35988.741348
17	93644.214511	61710.280900	38834.410599
18	108241.629592	69872.959802	42278.925271
19	122252.561920	77205.201100	46344.975129
20	140338.720955	86017.331219	50433.976843
21	159346.394715	94391.565536	57759.554918
22	183031.760801	105896.249972	62382.925298
23	206737.578813	117428.956925	68007.459915
24	227780.355225	129631.788433	74507.749867
25	258493.177545	143622.617795	81348.865066
26	286254.812126	157461.844804	90279.066427
27	324224.800046	172669.726129	99899.449433
28	367943.173037	192592.635302	108520.484803
29	412179.292166	214589.233102	122191.873320
30	467627.745574	240722.507959	133437.337804

PORTFOLIO ONLY IWDA